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Record W2594552425 · doi:10.1002/cjce.22819

Hydrodeoxygenation of guaiacol over molybdenum‐based catalysts: The effect of support and the nature of the active site

2017· article· en· W2594552425 on OpenAlexvenueno aff
Simone Ansaloni, Nunzio Russo, Raffaele Pirone

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHydrodeoxygenationGuaiacolCatalysisChemistryMolybdenumCarbon fibersPhysisorptionPhenolZeoliteX-ray photoelectron spectroscopyInorganic chemistryLigninAmmonium molybdateActivated carbonSulfidationSelectivityNuclear chemistryOrganic chemistryChemical engineeringMaterials scienceRaw materialAdsorptionComposite number

Abstract

fetched live from OpenAlex

Abstract The hydrodeoxygenation (HDO) of guaiacol has been chosen as a model process for the upgrading of lignin‐derived bio‐oils. Tests were carried out in a batch reactor at 350 °C, 4000 kPa of H 2 , in the presence of several Mo‐based catalysts prepared by impregnation of ammonium molybdate on SiO 2 , Al 2 O 3 , NaY zeolite, MgO, activated carbon, and graphite. These materials have been characterized by means of: N 2 physisorption, XRD, FESEM/EDS, XPS, TPR‐H 2 , and TPD‐NH 3 , with the aim of correlating the physical and chemical properties of the prepared samples with the resulting features in the HDO reaction. Mo on activated carbon showed the best performances towards guaiacol demethoxylation, exhibiting complete conversion, 72 % of selectivity to phenol, and 19 % to p‐ and o‐cresol. The high surface area and low acidity of activated carbon allow good dispersion of MoO x which exhibits characteristic fragments with a lamellar shape, able to provide a large active surface with localized acidity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.003
GPT teacher head0.189
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2017
Admission routes1
Has abstractyes

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